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Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy

Crossref DOI link: https://doi.org/10.1007/s12672-026-05126-6

Published Online: 2026-04-30

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Long, Shengrong

Xiao, Kewei

Hao, Zhipeng
Funding

Funding for this research was provided by:

China Postdoctoral Science Foundation (2025M772149)

Public Health Service Project of the Department of Finance of Hubei Province (SCZ2025016)
License Information

Text and Data Mining valid from 2026-04-30

Version of Record valid from 2026-06-18
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Article History

Received: 13 January 2026

Accepted: 27 April 2026

First Online: 30 April 2026

Declarations

:

: This study utilized publicly available data from TCGA, GEO, GTEx and other databases. All data were generated by the respective consortia with appropriate ethics approval and informed consent from participants. No additional ethics approval was required for this secondary analysis of de-identified public data. The study was conducted in accordance with the Declaration of Helsinki.

: The authors declare no competing interests.

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